Accurately determining demand forecasts for products are paramount concerns for retail organizations. Demand forecasts are used for inventory control, purchase planning, work force planning, and other planning needs of organizations. Inaccurate demand forecasts can result in shortages of inventory that are needed to meet current demand, which can result in lost sales and revenues for the organizations. Conversely, inventory that exceeds a current demand can adversely impact the profits of an organization. Excessive inventory of perishable goods may lead to a loss for those goods.
Teradata Corporation has developed a suite of analytical applications for the retail business, referred to as Teradata Demand Chain Management, that provides retailers with the tools they need for product demand forecasting, planning and replenishment. The Teradata Demand Chain Management solution provides a retailer with improved customer service levels and reductions in unproductive inventory, and is particularly adept at assisting a retailer forecast and plan for seasonal sales cycles. However, for many retailers the sales pattern of different products varies from day to day. Some products sell the same throughout a week while the sale of some products follows a certain pattern that, for example, might have higher sales over the weekend as compared to during the weekend. Holidays and seasons also affect the sales pattern for certain products. Before a long holiday, sales may be higher for some products, e.g., perishable goods, milk, soft drinks and other highly consumable items, because stores may be closed or shopping inconvenient for consumers. Most retailers and particularly Food and Grocery retailers need to accurately forecast daily sales in order to minimize store inventories and optimize store replenishment schedules.
The Teradata Demand Chain management solution generates product demand forecasts on a weekly basis. Daily weights (percentage sales of each day of the week) are used to decompose weekly time series forecasts into daily forecasts. For calculation of these weights, it is not sufficient to just average previous weekly daily sales patterns. It is believed that daily sales patterns changes seasonally, and that the corresponding week number from the previous year may give the best approximation of the daily weights for a current week's daily forecast. Also, more similarity may exist between alternate odd/even weeks than just the previous weeks. This may be due to government disbursements and bi-weekly pay days.
Presented below is an improved technique for calculation of future daily weights. The effects of the seasonality, odd/even weeks, and recent week sales values are automatically measured for a given dataset, and applied to the forecast of future daily weights.